CN111105418A - High-precision image segmentation method for rectangular target in image - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000003709 image segmentation Methods 0.000 title claims abstract description 22
- 230000009466 transformation Effects 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 12
- 238000012937 correction Methods 0.000 claims abstract description 11
- 238000012935 Averaging Methods 0.000 claims description 3
- 230000000877 morphologic effect Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 abstract description 15
- 230000001131 transforming effect Effects 0.000 abstract description 4
- 239000011159 matrix material Substances 0.000 abstract description 2
- 238000005401 electroluminescence Methods 0.000 description 10
- 230000007547 defect Effects 0.000 description 6
- 238000013135 deep learning Methods 0.000 description 5
- 238000001514 detection method Methods 0.000 description 5
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- 238000013528 artificial neural network Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20004—Adaptive image processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20036—Morphological image processing
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
Abstract
The invention provides a high-precision image segmentation method for a rectangular target in an image, which relates to the technical field of image processing, and comprises the following steps of 1: reading the image into a memory, and carrying out distortion correction on the image; step 2: determining four end points of the rectangular outline of the image after the distortion correction, and carrying out perspective transformation on the image; and step 3: and segmenting the image after perspective transformation to obtain a flat image. The rectangular outline area in the workpiece image is extracted by transforming and segmenting the rectangular workpiece image, and segmentation is carried out according to the internal grid of the rectangular workpiece image, so that the segmentation position is just positioned on the boundary of the matrix workpiece image or the grid boundary, and the accuracy of image segmentation is ensured.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a high-precision image segmentation method for a rectangular target in an image.
Background
At present, the industrial defect detection is to detect the defects of the appearance and the interior of an industrial product by utilizing a deep learning technology or a digital image processing technology and judge whether the product quality is qualified or not.
Before the solar photovoltaic module leaves a factory, the solar photovoltaic module needs to be electrified and an infrared image is shot, the process is called Electroluminescence (EL), and through the shooting of an EL image, the photovoltaic module is difficult to find on the appearance, but defects (such as hidden cracks, cold solder and the like) existing inside the solar photovoltaic module are difficult to show. In the deep learning technology, an image is automatically distinguished, defects in the image can be found and located, EL image segmentation is a difficult and very important operation in the development of an automatic detection algorithm, but segmentation is the first step of detection, and if the segmentation effect is not ideal, the detection progress is inevitably reduced.
The EL image is a non-standard shot, and radial distortion, rotation, displacement, and the like are generated, and currently employed segmentation methods include an image segmentation method based on template matching and an image segmentation method based on depth learning. As shown in fig. 1, the image segmentation method based on module matching is implemented by using a module matching segmentation algorithm, and an EL image is matched with a standard EL template image, so as to segment a target region; as shown in fig. 2, the image segmentation method based on the deep learning image is implemented by a Mask rcnn algorithm based on a deep neural network, and the EL image is subjected to network recognition by the trained Mask rcnn, so as to segment a target region.
The image segmentation method based on template matching has the advantages of simple application, but has the disadvantages of poor matching effect, easy occurrence of segmentation failure and the like, and can not meet the requirement of segmentation accuracy higher than 99.9% in the industrial production process; the image segmentation method based on the deep learning has the advantages that the segmentation is stable, the problems of segmentation failure and the like are not easy to occur, but the segmentation boundary has large fluctuation and low segmentation speed, the requirement on the running time of a production line cannot be met, and the requirement on the segmentation accuracy of more than 99.9% in the production process cannot be met.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention aims to provide a high-precision image segmentation method for a rectangular object in an image, which transforms and segments a rectangular workpiece image, extracts a rectangular outline region in the workpiece image, and segments the rectangular outline region according to an internal grid of the rectangular workpiece image, so as to ensure that the segmentation position is just on the boundary of a matrix workpiece image or the grid boundary, and ensure the accuracy of image segmentation.
The invention provides a high-precision image segmentation method for a rectangular target in an image, which comprises the following steps of:
step 1: reading the image into a memory, and carrying out distortion correction on the image;
step 2: determining four end points of the rectangular outline of the image after the distortion correction, and carrying out perspective transformation on the image;
and step 3: and segmenting the image after perspective transformation to obtain a flat image.
Further, the step of performing distortion correction on the image is as follows:
step 1.1: reading an image generated by the solar photovoltaic module through EL shooting into a memory through Opencv software to form a single-channel digital image;
step 1.2: pre-training distortion parameters of the digital image, and performing distortion restoration on the digital image by using the distortion parameters to obtain a digital image with straight edges;
step 1.3: carrying out self-adaptive binary processing on the digital image to obtain a binary image;
step 1.4: and carrying out iterative opening operation on the binary image to obtain a standard binary image.
Further, the step of perspective transforming the image is as follows:
step 2.1: performing morphological processing on the binary image, searching an image edge contour, and acquiring an edge linked list of a minimum circumscribed polygon of the edge contour;
step 2.2: searching two points with the farthest distance in the edge chain table, namely the diagonal line of the rectangular outline area; connecting the diagonals and searching two points with the maximum distance between the positive area and the negative area of the diagonals, namely the other diagonals of the rectangular outline area, namely determining four end points of the rectangular outline area;
step 2.3: and carrying out perspective transformation on the binary image according to four points of the rectangular outline area, standardizing perspective distortion to form a standard rectangular outline area, and carrying out bilinear interpolation processing on all points which cannot be subjected to perspective transformation in the binary image.
Further, the specific steps of segmenting the image are as follows:
step 3.1: respectively averaging the two-value images subjected to perspective transformation according to the horizontal and vertical axis directions to obtain average one-dimensional signals in the horizontal and vertical axis directions, searching position coordinates of all local minimum values by applying an interval minimum value algorithm, and connecting the position coordinates to obtain a dividing line;
step 3.2: comparing the dividing line with the actual specification of the solar photovoltaic module, if the number of the dividing line is not in accordance with the actual specification of the solar photovoltaic module, adjusting the parameters to try again, and repairing; and if the number is consistent, segmenting the binary image to obtain a flat image.
As described above, the high-precision image segmentation method for the rectangular object in the image according to the present invention has the following beneficial effects:
1. the invention solves the problem of image distortion caused by the position of a camera, the shooting angle and the like, and can output a straight rectangular target object.
2. The invention provides a positioning method based on four endpoints, which converts the region matching of images into the endpoint matching and improves the speed and the accuracy of the image matching.
Drawings
FIG. 1 is a flow chart of a segmentation method based on module matching disclosed in the prior art;
FIG. 2 is a flowchart illustrating a method for image segmentation based on deep learning image disclosed in the prior art;
FIG. 3 is a flowchart of an image segmentation method disclosed in an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
As shown in fig. 3, the present invention provides a high precision image segmentation method for a rectangular object in an image, the method comprising the following steps:
step 1: reading the image into a memory, and carrying out distortion correction on the image;
the steps of correcting the distortion of the image are as follows:
step 1.1: reading an image generated by the solar photovoltaic module through EL shooting into a memory through Opencv software to form a single-channel digital image;
step 1.2: pre-training distortion parameters of the digital image, and repairing the distortion of the image by using the distortion parameters to obtain a digital image with straight edges;
step 1.3: performing self-adaptive binary processing on the digital image with straight edges to obtain a binary image;
step 1.4: and performing iterative opening operation on the binary image to obtain a standard binary image, and ensuring that the gray value of the solar photovoltaic module area is 255 and the gray value outside the solar photovoltaic module area is 0.
Step 2: determining four end points of the rectangular outline of the image after the distortion correction, and carrying out perspective transformation on the image;
the steps of perspective transforming the image are as follows:
step 2.1: performing morphological processing on the binary image, searching an image edge contour, and acquiring an edge linked list of a minimum circumscribed polygon of the edge contour;
step 2.2: two points with the farthest distance are searched in the edge chain table, namely the diagonal lines of the rectangular outline area; connecting the diagonals and searching two points with the maximum distance between the positive area and the negative area of the diagonals, namely the other diagonals of the rectangular outline area, so that four end points of the rectangular outline area are determined;
step 2.3: carrying out perspective transformation on the standard binary image according to four end points of the rectangular outline region, standardizing perspective distortion to form a standard rectangular outline region, and carrying out bilinear interpolation processing on all points which cannot be subjected to perspective transformation in the standard binary image;
and step 3: segmenting the image after perspective transformation to obtain a flat image;
the steps of perspective transforming the image are as follows:
step 3.1: respectively averaging the two-value images subjected to perspective transformation according to the horizontal and vertical axis directions to obtain average one-dimensional signals in the horizontal and vertical axis directions, searching position coordinates of all local minimum values by applying an interval minimum value algorithm, and connecting the position coordinates to obtain a dividing line;
step 3.2: comparing the dividing line with the actual specification of the solar photovoltaic module, and if the number of the dividing line is not in accordance with the actual specification of the solar photovoltaic module, adjusting the parameters to try again to repair the solar photovoltaic module; and if the number is consistent, segmenting the binary image to obtain a flat image.
In summary, the invention makes full use of the characteristic information of the rectangular target, automatically performs distortion correction on the camera, positions the target area through four end points of the rectangular outline area, calculates and transforms the rectangular outline area, can not only segment the rectangular target object, but also correct image distortion caused by the camera position, the shooting angle and the like in the shooting process of the camera, finally obtains a straight rectangular target, and provides input meeting the requirements for industrial defect detection. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (4)
1. A high-precision image segmentation method for a rectangular object in an image is characterized by comprising the following steps:
step 1: reading the image into a memory, and carrying out distortion correction on the image;
step 2: determining four end points of the rectangular outline of the image after the distortion correction, and carrying out perspective transformation on the image;
and step 3: and segmenting the image after perspective transformation to obtain a flat image.
2. A method for segmenting an image into rectangular objects with high precision according to claim 1, wherein the step of performing distortion correction on the image is as follows:
step 1.1: reading an image generated by the solar photovoltaic module through EL shooting into a memory through Opencv software to form a single-channel digital image;
step 1.2: pre-training distortion parameters of the digital image, and performing distortion restoration on the digital image by using the distortion parameters to obtain a digital image with straight edges;
step 1.3: carrying out self-adaptive binary processing on the digital image to obtain a binary image;
step 1.4: and carrying out iterative opening operation on the binary image to obtain a standard binary image.
3. A method for high-precision image segmentation of a rectangular object in an image according to claim 2, wherein the step of perspective transformation of the image is as follows:
step 2.1: performing morphological processing on the binary image, searching an image edge contour, and acquiring an edge linked list of a minimum circumscribed polygon of the edge contour;
step 2.2: searching two points with the farthest distance in the edge chain table, namely the diagonal line of the rectangular outline area; connecting the diagonals and searching two points with the maximum distance between the positive area and the negative area of the diagonals, namely the other diagonals of the rectangular outline area, namely determining four end points of the rectangular outline area;
step 2.3: and carrying out perspective transformation on the binary image according to four end points of the rectangular outline region, standardizing perspective distortion to form a standard rectangular outline region, and carrying out bilinear interpolation processing on all points which cannot be subjected to perspective transformation in the binary image.
4. A high-precision image segmentation method for rectangular objects in images according to claim 3, characterized in that the specific steps of segmenting the image are as follows:
step 3.1: respectively averaging the two-value images subjected to perspective transformation according to the horizontal and vertical axis directions to obtain average one-dimensional signals in the horizontal and vertical axis directions, searching position coordinates of all local minimum values by applying an interval minimum value algorithm, and connecting the position coordinates to obtain a dividing line;
step 3.2: comparing the dividing line with the actual specification of the solar photovoltaic module, if the number of the dividing line is not in accordance with the actual specification of the solar photovoltaic module, adjusting the parameters to try again, and repairing; and if the number is consistent, segmenting the binary image to obtain a flat image.
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Denomination of invention: A high-precision image segmentation method for rectangular targets in images Effective date of registration: 20240103 Granted publication date: 20230711 Pledgee: Shanghai Rural Commercial Bank Co.,Ltd. Jiading sub branch Pledgor: Shanghai HONGPU Information Technology Co.,Ltd. Registration number: Y2024310000006 |